RT Journal
A1 Chuang, Trees-Juen
A1 Lin, Wen-Chang
A1 Lee, Hurng-Chun
A1 Wang, Chi-Wei
A1 Hsiao, Keh-Lin
A1 Wang, Zi-Hao
A1 Shieh, Danny
A1 Lin, Simon C.
A1 Ch'ang, Lan-Yang
T1 A Complexity Reduction Algorithm for Analysis and Annotation of Large Genomic Sequences
JF Genome Research 
JO Genome Research 
YR 2003 
FD February 01 
VO 13 
IS 2 
SP 313 
OP 322 
DO 10.1101/gr.313703 
UL http://genome.cshlp.org/content/13/2/313.abstract 
AB DNA is a universal language encrypted with biological instruction for life. In higher organisms, the genetic information is preserved predominantly in an organized exon/intron structure. When a gene is expressed, the exons are spliced together to form the transcript for protein synthesis. We have developed a complexity reduction algorithm for sequence analysis (CRASA) that enables direct alignment of cDNA sequences to the genome. This method features a progressive data structure in hierarchical orders to facilitate a fast and efficient search mechanism. CRASA implementation was tested with already annotated genomic sequences in two benchmark data sets and compared with 15 annotation programs (10 ab initio and 5 homology-based approaches) against the EST database. By the use of layered noise filters, the complexity of CRASA-matched data was reduced exponentially. The results from the benchmark tests showed that CRASA annotation excelled in both the sensitivity and specificity categories. When CRASA was applied to the analysis of human Chromosomes 21 and 22, an additional 83 potential genes were identified. With its large-scale processing capability, CRASA can be used as a robust tool for genome annotation with high accuracy by matching the EST sequences precisely to the genomic sequences.[Supplementary material is available online at http://www.genome.organdhttp://crasa.sinica.edu.tw/bioinformatics/Supplementary.htm.]